Analysis of nonlinear behavior of viscoelastic damping in musical membranes using physics-informed self-organizing maps
This research offers a new analytical tool that unravels the nonlinear relation between the parameters of Viscoelastic Damping (VD) and the resulting frequency spectrum in musical membranes. Understanding how variations in VD parameters influence the resulting sounds is crucial for developing new to...
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-03-01
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| Series: | APL Machine Learning |
| Online Access: | http://dx.doi.org/10.1063/5.0242985 |
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| author | Cristhiam Fidel Martínez Orellanos Rolf Bader |
| author_facet | Cristhiam Fidel Martínez Orellanos Rolf Bader |
| author_sort | Cristhiam Fidel Martínez Orellanos |
| collection | DOAJ |
| description | This research offers a new analytical tool that unravels the nonlinear relation between the parameters of Viscoelastic Damping (VD) and the resulting frequency spectrum in musical membranes. Understanding how variations in VD parameters influence the resulting sounds is crucial for developing new tools for artistic expression and for designing musical instruments with distinct sound qualities. In the case of membranophones, the external damping is well understood, while the internal damping due to viscoelastic properties of materials remains unclear. In previous research, VD in musical membranes has been modeled using a Finite-Difference Time-Domain (FDTD) model. Nonetheless, analyzing the complex relationships between the large parameter space of the model and the nonlinear behavior of VD is a challenging task. This study addresses this analysis through physics-based machine learning. We employed a FDTD model of a viscoelastically damped membrane to create a physics-informed dataset, which we subsequently analyzed using Self-Organizing Maps (SOMs). Our findings reveal that the damping coefficient is the primary criterion when clustering the data. Furthermore, we found the internal structure of the cluster to depend on the rate of decay of the memory effect, i.e., the rate at which the energy introduced back into the system decreases. The study also demonstrates the benefits of using principal component analysis for the SOM initialization. |
| format | Article |
| id | doaj-art-b5ade42e4fc44e9abd49ebf16c1cb415 |
| institution | OA Journals |
| issn | 2770-9019 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | APL Machine Learning |
| spelling | doaj-art-b5ade42e4fc44e9abd49ebf16c1cb4152025-08-20T01:55:49ZengAIP Publishing LLCAPL Machine Learning2770-90192025-03-0131016102016102-1510.1063/5.0242985Analysis of nonlinear behavior of viscoelastic damping in musical membranes using physics-informed self-organizing mapsCristhiam Fidel Martínez Orellanos0Rolf Bader1Institute of Systematic Musicology, University of Hamburg, Hamburg 20354, GermanyInstitute of Systematic Musicology, University of Hamburg, Hamburg 20354, GermanyThis research offers a new analytical tool that unravels the nonlinear relation between the parameters of Viscoelastic Damping (VD) and the resulting frequency spectrum in musical membranes. Understanding how variations in VD parameters influence the resulting sounds is crucial for developing new tools for artistic expression and for designing musical instruments with distinct sound qualities. In the case of membranophones, the external damping is well understood, while the internal damping due to viscoelastic properties of materials remains unclear. In previous research, VD in musical membranes has been modeled using a Finite-Difference Time-Domain (FDTD) model. Nonetheless, analyzing the complex relationships between the large parameter space of the model and the nonlinear behavior of VD is a challenging task. This study addresses this analysis through physics-based machine learning. We employed a FDTD model of a viscoelastically damped membrane to create a physics-informed dataset, which we subsequently analyzed using Self-Organizing Maps (SOMs). Our findings reveal that the damping coefficient is the primary criterion when clustering the data. Furthermore, we found the internal structure of the cluster to depend on the rate of decay of the memory effect, i.e., the rate at which the energy introduced back into the system decreases. The study also demonstrates the benefits of using principal component analysis for the SOM initialization.http://dx.doi.org/10.1063/5.0242985 |
| spellingShingle | Cristhiam Fidel Martínez Orellanos Rolf Bader Analysis of nonlinear behavior of viscoelastic damping in musical membranes using physics-informed self-organizing maps APL Machine Learning |
| title | Analysis of nonlinear behavior of viscoelastic damping in musical membranes using physics-informed self-organizing maps |
| title_full | Analysis of nonlinear behavior of viscoelastic damping in musical membranes using physics-informed self-organizing maps |
| title_fullStr | Analysis of nonlinear behavior of viscoelastic damping in musical membranes using physics-informed self-organizing maps |
| title_full_unstemmed | Analysis of nonlinear behavior of viscoelastic damping in musical membranes using physics-informed self-organizing maps |
| title_short | Analysis of nonlinear behavior of viscoelastic damping in musical membranes using physics-informed self-organizing maps |
| title_sort | analysis of nonlinear behavior of viscoelastic damping in musical membranes using physics informed self organizing maps |
| url | http://dx.doi.org/10.1063/5.0242985 |
| work_keys_str_mv | AT cristhiamfidelmartinezorellanos analysisofnonlinearbehaviorofviscoelasticdampinginmusicalmembranesusingphysicsinformedselforganizingmaps AT rolfbader analysisofnonlinearbehaviorofviscoelasticdampinginmusicalmembranesusingphysicsinformedselforganizingmaps |